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- Ameline, Baptiste, et al.
(författare)
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Methylation and copy number profiling : emerging tools to differentiate osteoblastoma from malignant mimics?
- 2022
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Ingår i: Modern Pathology. - : Elsevier BV. - 0893-3952. ; 35:9, s. 1204-1211
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Tidskriftsartikel (refereegranskat)abstract
- Rearrangements of the transcription factors FOS and FOSB have recently been identified as the genetic driver event underlying osteoid osteoma and osteoblastoma. Nuclear overexpression of FOS and FOSB have since then emerged as a reliable surrogate marker despite limitations in specificity and sensitivity. Indeed, osteosarcoma can infrequently show nuclear FOS expression and a small fraction of osteoblastomas seem to arise independent of FOS/FOSB rearrangements. Acid decalcification and tissue preservation are additional factors that can negatively influence immunohistochemical testing and make diagnostic decision-making challenging in individual cases. Particularly aggressive appearing osteoblastomas, also referred to as epithelioid osteoblastomas, and osteoblastoma-like osteosarcoma can be difficult to distinguish, underlining the need for additional markers to support the diagnosis. Methylation and copy number profiling, a technique well established for the classification of brain tumors, might fill this gap. Here, we set out to comprehensively characterize a series of 77 osteoblastomas by immunohistochemistry, fluorescence in-situ hybridization as well as copy number and methylation profiling and compared our findings to histologic mimics. Our results show that osteoblastomas are uniformly characterized by flat copy number profiles that can add certainty in reaching the correct diagnosis. The methylation cluster formed by osteoblastomas, however, so far lacks specificity and can be misleading in individual cases.
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| 3. |
- Asa, Sylvia L., et al.
(författare)
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Pituitary neuroendocrine tumors : a model for neuroendocrine tumor classification
- 2021
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Ingår i: Modern Pathology. - : Springer Nature. - 0893-3952 .- 1530-0285. ; 34:9, s. 1634-1650
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Forskningsöversikt (refereegranskat)abstract
- The classification of adenohypophysial neoplasms as "pituitary neuroendocrine tumors" (PitNETs) was proposed in 2017 to reflect their characteristics as epithelial neuroendocrine neoplasms with a spectrum of clinical behaviors ranging from small indolent lesions to large, locally invasive, unresectable tumors. Tumor growth and hormone hypersecretion cause significant morbidity and mortality in a subset of patients. The proposal was endorsed by a WHO working group that sought to provide a unified approach to neuroendocrine neoplasia in all body sites. We review the features that are characteristic of neuroendocrine cells, the epidemiology and prognosis of these tumors, as well as further refinements in terms used for other pituitary tumors to ensure consistency with the WHO framework. The intense study of PitNETs has provided information about the importance of cellular differentiation in tumor prognosis as a model for neuroendocrine tumors in different locations.
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| 5. |
- Bokhorst, J. M., et al.
(författare)
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Assessment of individual tumor buds using keratin immunohistochemistry: moderate interobserver agreement suggests a role for machine learning
- 2020
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Ingår i: Modern Pathology. - : NATURE PUBLISHING GROUP. - 0893-3952 .- 1530-0285. ; 33:5, s. 825-833
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Tidskriftsartikel (refereegranskat)abstract
- Tumor budding is a promising and cost-effective biomarker with strong prognostic value in colorectal cancer. However, challenges related to interobserver variability persist. Such variability may be reduced by immunohistochemistry and computer-aided tumor bud selection. Development of computer algorithms for this purpose requires unequivocal examples of individual tumor buds. As such, we undertook a large-scale, international, and digital observer study on individual tumor bud assessment. From a pool of 46 colorectal cancer cases with tumor budding, 3000 tumor bud candidates were selected, largely based on digital image analysis algorithms. For each candidate bud, an image patch (size 256 x 256 mu m) was extracted from a pan cytokeratin-stained whole-slide image. Members of an International Tumor Budding Consortium (n = 7) were asked to categorize each candidate as either (1) tumor bud, (2) poorly differentiated cluster, or (3) neither, based on current definitions. Agreement was assessed with Cohens and Fleiss Kappa statistics. Fleiss Kappa showed moderate overall agreement between observers (0.42 and 0.51), while Cohens Kappas ranged from 0.25 to 0.63. Complete agreement by all seven observers was present for only 34% of the 3000 tumor bud candidates, while 59% of the candidates were agreed on by at least five of the seven observers. Despite reports of moderate-to-substantial agreement with respect to tumor budding grade, agreement with respect to individual pan cytokeratin-stained tumor buds is moderate at most. A machine learning approach may prove especially useful for a more robust assessment of individual tumor buds.
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| 7. |
- Bokhorst, John-Melle, et al.
(författare)
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Fully Automated Tumor Bud Assessment in Hematoxylin and Eosin-Stained Whole Slide Images of Colorectal Cancer
- 2023
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Ingår i: Modern Pathology. - : ELSEVIER SCIENCE INC. - 0893-3952 .- 1530-0285. ; 36:9
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Tidskriftsartikel (refereegranskat)abstract
- Tumor budding (TB), the presence of single cells or small clusters of up to 4 tumor cells at the invasive front of colorectal cancer (CRC), is a proven risk factor for adverse outcomes. International definitions are necessary to reduce interobserver variability. According to the current international guidelines, hotspots at the invasive front should be counted in hematoxylin and eosin (H & E)-stained slides. This is time-consuming and prone to interobserver variability; therefore, there is a need for computer-aided diagnosis solutions. In this study, we report an artificial intelligence-based method for detecting TB in H & E-stained whole slide images. We propose a fully automated pipeline to identify the tumor border, detect tumor buds, characterize them based on the number of tumor cells, and produce a TB density map to identify the TB hotspot. The method outputs the TB count in the hotspot as a computational biomarker. We show that the proposed automated TB detection workflow performs on par with a panel of 5 pathologists at detecting tumor buds and that the hotspot-based TB count is an independent prognosticator in both the univariate and the multivariate analysis, validated on a cohort of n 1/4 981 patients with CRC. Computer-aided detection of tumor buds based on deep learning can perform on par with expert pathologists for the detection and quantification of tumor buds in H & E-stained CRC histopathology slides, strongly facilitating the introduction of budding as an independent prognosticator in clinical routine and clinical trials. & COPY; 2023 THE AUTHORS. Published by Elsevier Inc. on behalf of the United States & Canadian Academy of Pathology. This is an open access article under the CC BY license (http://creativecommons.org/ licenses/by/4.0/).
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| 8. |
- Bulten, Wouter, et al.
(författare)
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Artificial intelligence assistance significantly improves Gleason grading of prostate biopsies by pathologists
- 2021
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Ingår i: Modern Pathology. - : NATURE PUBLISHING GROUP. - 0893-3952 .- 1530-0285. ; 34, s. 660-671
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Tidskriftsartikel (refereegranskat)abstract
- The Gleason score is the most important prognostic marker for prostate cancer patients, but it suffers from significant observer variability. Artificial intelligence (AI) systems based on deep learning can achieve pathologist-level performance at Gleason grading. However, the performance of such systems can degrade in the presence of artifacts, foreign tissue, or other anomalies. Pathologists integrating their expertise with feedback from an AI system could result in a synergy that outperforms both the individual pathologist and the system. Despite the hype around AI assistance, existing literature on this topic within the pathology domain is limited. We investigated the value of AI assistance for grading prostate biopsies. A panel of 14 observers graded 160 biopsies with and without AI assistance. Using AI, the agreement of the panel with an expert reference standard increased significantly (quadratically weighted Cohens kappa, 0.799 vs. 0.872;p = 0.019). On an external validation set of 87 cases, the panel showed a significant increase in agreement with a panel of international experts in prostate pathology (quadratically weighted Cohens kappa, 0.733 vs. 0.786;p = 0.003). In both experiments, on a group-level, AI-assisted pathologists outperformed the unassisted pathologists and the standalone AI system. Our results show the potential of AI systems for Gleason grading, but more importantly, show the benefits of pathologist-AI synergy.
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| 9. |
- Cloutier, Jeffrey M., et al.
(författare)
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“Inflammatory Leiomyosarcoma” and “Histiocyte-rich Rhabdomyoblastic Tumor” : a clinicopathological, immunohistochemical and genetic study of 13 cases, with a proposal for reclassification as “Inflammatory Rhabdomyoblastic Tumor”
- 2021
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Ingår i: Modern Pathology. - : Elsevier BV. - 0893-3952. ; 34:4, s. 758-769
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Tidskriftsartikel (refereegranskat)abstract
- Inflammatory leiomyosarcoma (ILMS), defined as “a malignant neoplasm showing smooth muscle differentiation, a prominent inflammatory infiltrate, and near-haploidization”, is a very rare soft tissue tumor with a generally favorable prognosis. The morphologic features of “histiocyte-rich rhabdomyoblastic tumor” (HRRMT) are similar to those of ILMS, although this lesion shows by definition a skeletal muscle phenotype. Recent gene expression profiling and immunohistochemical studies have also suggested that ILMS and HRRMT may be related. We studied the clinicopathologic, immunohistochemical and genetic features of four cases previously classified as ILMS and nine classified as HRRMT. Tumors from both groups tended to occur in the deep soft tissues of the extremities of young to middle-aged males and exhibited indolent behavior. Morphologically, all were well-circumscribed, often encapsulated, and showed a striking histiocyte-rich inflammatory infiltrate admixed with variably pleomorphic tumor cells showing spindled and epithelioid to rhabdoid morphology, eosinophilic cytoplasm, and prominent nucleoli, but few, if any, mitotic figures. Immunohistochemically, the tumor cells expressed desmin, alpha-smooth muscle actin, and the rhabdomyoblastic markers PAX7, MyoD1, and myogenin. H-caldesmon expression was absent in all cases, using the specific h-CD antibody. Karyotypic study (1 HRRMT) and genome-wide copy number analysis (7 HRRMT, OncoScan SNP assay), revealed near-haploidization in four cases, with subsequent genome doubling in one, an identical phenotype to that seen in ILMS. We propose reclassification of ILMS and HRRMT as “inflammatory rhabdomyoblastic tumor”, a name which accurately describes the salient morphologic and immunohistochemical features of this distinctive tumor, as well as its intermediate (rarely metastasizing) clinical behavior.
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